miχpods - TAO

• eulerian 0-40m control volume

• focus on Jq

• use model chl / double exponentials

χpod notes:

• eps = 0 at -39

• 119m in 2015

%load_ext watermark

import os

import cf_xarray  # nqoa
import dask
import dcpy
import distributed
import flox.xarray
import hvplot.xarray
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
import dcpy
import glob


import pump
from pump import mixpods

dask.config.set({"array.slicing.split_large_chunks": False})
mpl.rcParams["figure.dpi"] = 140
xr.set_options(keep_attrs=True)

gcmdir = "/glade/campaign/cgd/oce/people/bachman/TPOS_1_20_20_year/OUTPUT/"  # MITgcm output directory
stationdirname = gcmdir

%watermark -iv

import dask_jobqueue

if "client" in locals():
    client.close()
    del client
if "cluster" in locals():
    cluster.close()

cluster = dask_jobqueue.PBSCluster(
    cores=4,  # The number of cores you want
    memory="23GB",  # Amount of memory
    processes=1,  # How many processes
    queue="casper",  # The type of queue to utilize (/glade/u/apps/dav/opt/usr/bin/execcasper)
    local_directory="/local_scratch/pbs.$PBS_JOBID",
    log_directory="/glade/scratch/dcherian/dask/",  # Use your local directory
    resource_spec="select=1:ncpus=4:mem=23GB",  # Specify resources
    project="ncgd0011",  # Input your project ID here
    walltime="02:00:00",  # Amount of wall time
    interface="ib0",  # Interface to use
)
cluster.adapt(minimum_jobs=2, maximum_jobs=8)
# cluster.scale(4)
client = distributed.Client(cluster)
client

%autoreload

tao_gridded = mixpods.load_tao()
tao_gridded["Ih"] = 0.45 * tao_gridded.swnet * np.exp(-0.04 * np.abs(tao_gridded.mldT))

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:256: UserWarning: The specified Dask chunks separate the stored chunks along dimension "depth" starting at index 42. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:256: UserWarning: The specified Dask chunks separate the stored chunks along dimension "time" starting at index 199726. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:256: UserWarning: The specified Dask chunks separate the stored chunks along dimension "longitude" starting at index 2. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(

tao_gridded["Ih"] = 0.45 * tao_gridded.swnet * np.exp(-0.04 * np.abs(tao_gridded.mldT))
tao_gridded.Ih.groupby("time.month").mean().plot()
tao_gridded["Ih"] = 0.45 * tao_gridded.swnet * np.exp(-0.04 * 15)
tao_gridded.Ih.groupby("time.month").mean().plot()

[<matplotlib.lines.Line2D at 0x2af62c82d2d0>]

Check masking

From Sally:

ind = find(chihr.time > datenum(2007,1,14,10,25,0),1,'first');
chihr.Jq(3,ind) = NaN;
chihr.eps(3,ind) = NaN;
chihr.Kt(3,ind) = NaN;

ind = find(chihr.time > datenum(2007,1,28,14,25,0),1,'first');
chihr.Jq(4,ind) = NaN;
chihr.eps(4,ind) = NaN;
chihr.Kt(4,ind) = NaN;

%autoreload

chi = mixpods.read_chipod_mat_file(
    os.path.expanduser("~/work/pump/datasets/microstructure/chipods_0_140W_hourly.mat")
)

chi

<xarray.Dataset>
Dimensions:  (depth: 7, time: 128756)
Coordinates:
  * depth    (depth) float64 29.0 39.0 49.0 59.0 69.0 89.0 119.0
  * time     (time) datetime64[ns] 2005-09-23T04:30:00 ... 2020-05-31T23:30:00
Data variables:
    Jq       (time, depth) float64 -4.549 nan -0.0657 nan ... nan nan nan nan
    KT       (time, depth) float64 9.694e-05 nan 1.4e-06 nan ... nan nan nan nan
    N2       (time, depth) float64 3.798e-05 nan 4.135e-05 nan ... nan nan nan
    T        (time, depth) float64 25.56 nan 25.31 nan nan ... nan nan nan nan
    chi      (time, depth) float64 2.708e-08 nan 4.073e-10 nan ... nan nan nan
    dTdz     (time, depth) float64 0.01282 nan 0.01419 nan ... nan nan nan nan
    eps      (time, depth) float64 1.657e-08 nan 2.357e-10 nan ... nan nan nan

xarray.Dataset

• Dimensions:
  • depth: 7
  • time: 128756
• Coordinates: (2)
  • depth
    (depth)
    float64
    29.0 39.0 49.0 59.0 69.0 89.0 119.0

    axis :

    Z

    positive :

    down

    array([ 29.,  39.,  49.,  59.,  69.,  89., 119.])

  • time
    (time)
    datetime64[ns]
    2005-09-23T04:30:00 ... 2020-05-...

    axis :

    T

    standard_name :

    time

    array(['2005-09-23T04:30:00.000000000', '2005-09-23T05:30:00.000000000',
           '2005-09-23T06:30:00.000000000', ..., '2020-05-31T21:30:00.000000000',
           '2020-05-31T22:30:00.000000000', '2020-05-31T23:30:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (7)
  • Jq
    (time, depth)
    float64
    -4.549 nan -0.0657 ... nan nan nan

    array([[ -4.54932371,          nan,  -0.06569762, ...,          nan,
             -1.85457704,          nan],
           [-16.37203995,          nan,  -0.080956  , ...,          nan,
             -5.2049935 ,          nan],
           [-23.9366487 ,          nan,  -0.07778456, ...,          nan,
             -2.22962776,          nan],
           ...,
           [         nan,          nan,          nan, ...,          nan,
                     nan,          nan],
           [         nan,          nan,          nan, ...,          nan,
                     nan,          nan],
           [         nan,          nan,          nan, ...,          nan,
                     nan,          nan]])

  • KT
    (time, depth)
    float64
    9.694e-05 nan 1.4e-06 ... nan nan

    standard_name :

    ocean_vertical_heat_diffusivity

    array([[9.69412508e-05,            nan, 1.40005393e-06, ...,
                       nan, 8.04935155e-06,            nan],
           [3.95227442e-04,            nan, 1.49476348e-06, ...,
                       nan, 2.38733489e-05,            nan],
           [7.45841293e-04,            nan, 1.60608911e-06, ...,
                       nan, 1.18334467e-05,            nan],
           ...,
           [           nan,            nan,            nan, ...,
                       nan,            nan,            nan],
           [           nan,            nan,            nan, ...,
                       nan,            nan,            nan],
           [           nan,            nan,            nan, ...,
                       nan,            nan,            nan]])

  • N2
    (time, depth)
    float64
    3.798e-05 nan 4.135e-05 ... nan nan

    array([[3.79834246e-05,            nan, 4.13460771e-05, ...,
                       nan, 1.65159681e-04,            nan],
           [4.15890454e-05,            nan, 4.44176951e-05, ...,
                       nan, 1.72427071e-04,            nan],
           [3.28784995e-05,            nan, 4.21102361e-05, ...,
                       nan, 1.53967780e-04,            nan],
           ...,
           [           nan,            nan,            nan, ...,
                       nan,            nan,            nan],
           [           nan,            nan,            nan, ...,
                       nan,            nan,            nan],
           [           nan,            nan,            nan, ...,
                       nan,            nan,            nan]])

  • T
    (time, depth)
    float64
    25.56 nan 25.31 nan ... nan nan nan

    array([[25.56315052,         nan, 25.31183159, ...,         nan,
            24.39803403,         nan],
           [25.56676681,         nan, 25.30604519, ...,         nan,
            24.35174395,         nan],
           [25.53080021,         nan, 25.29494317, ...,         nan,
            24.33841534,         nan],
           ...,
           [        nan,         nan,         nan, ...,         nan,
                    nan,         nan],
           [        nan,         nan,         nan, ...,         nan,
                    nan,         nan],
           [        nan,         nan,         nan, ...,         nan,
                    nan,         nan]])

  • chi
    (time, depth)
    float64
    2.708e-08 nan 4.073e-10 ... nan nan

    array([[2.70762728e-08,            nan, 4.07340582e-10, ...,
                       nan, 5.20677144e-08,            nan],
           [9.25025624e-08,            nan, 5.86562376e-10, ...,
                       nan, 1.40759953e-07,            nan],
           [1.04674898e-07,            nan, 5.24675110e-10, ...,
                       nan, 5.66922996e-08,            nan],
           ...,
           [           nan,            nan,            nan, ...,
                       nan,            nan,            nan],
           [           nan,            nan,            nan, ...,
                       nan,            nan,            nan],
           [           nan,            nan,            nan, ...,
                       nan,            nan,            nan]])

  • dTdz
    (time, depth)
    float64
    0.01282 nan 0.01419 ... nan nan nan

    array([[0.01281748,        nan, 0.01419076, ...,        nan, 0.0581683 ,
                   nan],
           [0.01402541,        nan, 0.01530269, ...,        nan, 0.06086101,
                   nan],
           [0.0110931 ,        nan, 0.01450203, ...,        nan, 0.05424165,
                   nan],
           ...,
           [       nan,        nan,        nan, ...,        nan,        nan,
                   nan],
           [       nan,        nan,        nan, ...,        nan,        nan,
                   nan],
           [       nan,        nan,        nan, ...,        nan,        nan,
                   nan]])

  • eps
    (time, depth)
    float64
    1.657e-08 nan 2.357e-10 ... nan nan

    array([[1.65670857e-08,            nan, 2.35675866e-10, ...,
                       nan, 6.47865832e-09,            nan],
           [6.42763900e-08,            nan, 2.92546293e-10, ...,
                       nan, 1.83704474e-08,            nan],
           [1.00581787e-07,            nan, 2.79577757e-10, ...,
                       nan, 7.79422764e-09,            nan],
           ...,
           [           nan,            nan,            nan, ...,
                       nan,            nan,            nan],
           [           nan,            nan,            nan, ...,
                       nan,            nan,            nan],
           [           nan,            nan,            nan, ...,
                       nan,            nan,            nan]])

• Indexes: (2)
  • depth
    PandasIndex

    PandasIndex(Float64Index([29.0, 39.0, 49.0, 59.0, 69.0, 89.0, 119.0], dtype='float64', name='depth'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2005-09-23 04:30:00', '2005-09-23 05:30:00',
                   '2005-09-23 06:30:00', '2005-09-23 07:30:00',
                   '2005-09-23 08:30:00', '2005-09-23 09:30:00',
                   '2005-09-23 10:30:00', '2005-09-23 11:30:00',
                   '2005-09-23 12:30:00', '2005-09-23 13:30:00',
                   ...
                   '2020-05-31 14:30:00', '2020-05-31 15:30:00',
                   '2020-05-31 16:30:00', '2020-05-31 17:30:00',
                   '2020-05-31 18:30:00', '2020-05-31 19:30:00',
                   '2020-05-31 20:30:00', '2020-05-31 21:30:00',
                   '2020-05-31 22:30:00', '2020-05-31 23:30:00'],
                  dtype='datetime64[ns]', name='time', length=128756, freq=None))

• Attributes: (0)

import holoviews as hv

kwargs = dict(groupby="time.year", by="depth", grid=True, aspect=10, frame_height=100)
(
    # chi.chi.hvplot.line(**kwargs, logy=True, ylim=(1e-11, 1e-3))
    chi.eps.hvplot.line(**kwargs, logy=True, ylim=(1e-11, 1e-3))
    + chi.KT.hvplot.line(**kwargs, logy=True, ylim=(1e-11, 10))
    + chi.Jq.hvplot.line(**kwargs)
    + chi.dTdz.hvplot.line(**kwargs)
).opts(
    hv.opts.Overlay(legend_cols=2, legend_position="top"),
    hv.opts.Layout(sizing_mode="stretch_width"),
).cols(
    1
)

ε-Ri histograms

• I think I’m missing some low Ri values.

%autoreload

tao_gridded = mixpods.load_tao()

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:256: UserWarning: The specified Dask chunks separate the stored chunks along dimension "depth" starting at index 42. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:256: UserWarning: The specified Dask chunks separate the stored chunks along dimension "time" starting at index 199726. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:256: UserWarning: The specified Dask chunks separate the stored chunks along dimension "longitude" starting at index 2. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(

tao_gridded.update(
    mixpods.pdf_N2S2(tao_gridded.sel(depthchi=slice(-69, -29), time=slice("2017")))
)

<xarray.Dataset>
Dimensions:                (time: 212302, depth: 61, depthchi: 7, N2T_bins: 29,
                            S2_bins: 29, enso_transition_phase: 7, stat: 2,
                            N2_bins: 29, Rig_T_bins: 9)
Coordinates: (12/26)
    deepest                (time) float64 dask.array<chunksize=(212302,), meta=np.ndarray>
  * depth                  (depth) float64 -300.0 -295.0 -290.0 ... -5.0 0.0
    eucmax                 (time) float64 dask.array<chunksize=(33130,), meta=np.ndarray>
    latitude               float32 0.0
    longitude              float32 -140.0
    mld                    (time) float64 dask.array<chunksize=(212302,), meta=np.ndarray>
    ...                     ...
  * S2_bins                (S2_bins) object [-5.0, -4.9) ... [-2.200000000000...
  * enso_transition_phase  (enso_transition_phase) object 'none' ... 'all'
  * stat                   (stat) object 'mean' 'count'
  * N2_bins                (N2_bins) object [-5.0, -4.9) ... [-2.200000000000...
    bin_areas              (N2T_bins, S2_bins) float64 0.01 0.01 ... 0.01 0.01
  * Rig_T_bins             (Rig_T_bins) object (0.025118864315095794, 0.03981...
Data variables: (12/41)
    N2                     (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
    N2T                    (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
    Ri                     (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
    Rig_T                  (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
    S                      (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
    S2                     (time, depth) float32 dask.array<chunksize=(33130, 61), meta=np.ndarray>
    ...                     ...
    Tflx_dia_diff          (time, depthchi) float64 nan nan nan ... nan nan nan
    Jb                     (time, depthchi, depth) float64 dask.array<chunksize=(33130, 7, 61), meta=np.ndarray>
    Rif                    (time, depthchi, depth) float64 dask.array<chunksize=(33130, 7, 61), meta=np.ndarray>
    n2s2pdf                (N2T_bins, S2_bins, enso_transition_phase) float64 dask.array<chunksize=(29, 29, 1), meta=np.ndarray>
    eps_n2s2               (stat, N2_bins, S2_bins, enso_transition_phase) float64 dask.array<chunksize=(1, 29, 29, 7), meta=np.ndarray>
    eps_ri                 (stat, Rig_T_bins, enso_transition_phase) float64 dask.array<chunksize=(1, 9, 1), meta=np.ndarray>
Attributes:
    CREATION_DATE:                23:26 24-FEB-2021
    Data_Source:                  Global Tropical Moored Buoy Array Project O...
    File_info:                    Contact: Dai.C.McClurg@noaa.gov
    Request_for_acknowledgement:  If you use these data in publications or pr...
    _FillValue:                   1.0000000409184788e+35
    array:                        TAO/TRITON
    missing_value:                1.0000000409184788e+35
    platform_code:                0n165e
    site_code:                    0n165e
    wmo_platform_code:            52321

xarray.Dataset

• Dimensions:
  • time: 212302
  • depth: 61
  • depthchi: 7
  • N2T_bins: 29
  • S2_bins: 29
  • enso_transition_phase: 7
  • stat: 2
  • N2_bins: 29
  • Rig_T_bins: 9
• Coordinates: (26)
  • deepest
    (time)
    float64
    dask.array<chunksize=(212302,), meta=np.ndarray>

    description :

    Deepest depth with a valid observation

    units :

    m

    Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

  • depth
    (depth)
    float64
    -300.0 -295.0 -290.0 ... -5.0 0.0

    axis :

    Z

    positive :

    up

    units :

    m

    array([-300., -295., -290., -285., -280., -275., -270., -265., -260., -255.,
           -250., -245., -240., -235., -230., -225., -220., -215., -210., -205.,
           -200., -195., -190., -185., -180., -175., -170., -165., -160., -155.,
           -150., -145., -140., -135., -130., -125., -120., -115., -110., -105.,
           -100.,  -95.,  -90.,  -85.,  -80.,  -75.,  -70.,  -65.,  -60.,  -55.,
            -50.,  -45.,  -40.,  -35.,  -30.,  -25.,  -20.,  -15.,  -10.,   -5.,
              0.])

  • eucmax
    (time)
    float64
    dask.array<chunksize=(33130,), meta=np.ndarray>

    units :

    m

    long_name :

    EUC maximum

    positive :

    up

    Array Chunk Bytes 1.62 MiB 781.25 kiB Shape (212302,) (100000,) Dask graph 3 chunks in 17 graph layers Data type float64 numpy.ndarray

  • latitude
    ()
    float32
    0.0

    array(0., dtype=float32)

  • longitude
    ()
    float32
    -140.0

    array(-140., dtype=float32)

  • mld
    (time)
    float64
    dask.array<chunksize=(212302,), meta=np.ndarray>

    long_name :

    $z_{MLD}$

    units :

    m

    Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

  • mldT
    (time)
    float64
    dask.array<chunksize=(33130,), meta=np.ndarray>

    long_name :

    MLD$_θ$

    units :

    m

    description :

    Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

    Array Chunk Bytes 1.62 MiB 781.25 kiB Shape (212302,) (100000,) Dask graph 3 chunks in 20 graph layers Data type float64 numpy.ndarray

  • reference_pressure
    ()
    int64
    0

    array(0)

  • shallowest
    (time)
    float64
    dask.array<chunksize=(212302,), meta=np.ndarray>

    Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

  • time
    (time)
    datetime64[ns]
    1996-01-01 ... 2020-03-20T21:00:00

    array(['1996-01-01T00:00:00.000000000', '1996-01-01T01:00:00.000000000',
           '1996-01-01T02:00:00.000000000', ..., '2020-03-20T19:00:00.000000000',
           '2020-03-20T20:00:00.000000000', '2020-03-20T21:00:00.000000000'],
          dtype='datetime64[ns]')

  • zeuc
    (depth, time)
    float64
    dask.array<chunksize=(42, 132856), meta=np.ndarray>

    Array Chunk Bytes 98.80 MiB 42.57 MiB Shape (61, 212302) (42, 132856) Dask graph 4 chunks in 3 graph layers Data type float64 numpy.ndarray

  • depthchi
    (depthchi)
    float64
    -119.0 -89.0 -69.0 ... -39.0 -29.0

    axis :

    Z

    positive :

    up

    array([-119.,  -89.,  -69.,  -59.,  -49.,  -39.,  -29.])

  • dcl_mask
    (depth, time)
    bool
    dask.array<chunksize=(61, 33130), meta=np.ndarray>

    description :

    True when 5m below mldT and above eucmax.

    Array Chunk Bytes 12.35 MiB 5.82 MiB Shape (61, 212302) (61, 100000) Dask graph 3 chunks in 45 graph layers Data type bool numpy.ndarray

  • oni
    (time)
    float32
    -0.9 -0.9 -0.9 -0.9 ... nan nan nan

    array([-0.9, -0.9, -0.9, ...,  nan,  nan,  nan], dtype=float32)

  • en_mask
    (time)
    bool
    False False False ... False False

    array([False, False, False, ..., False, False, False])

  • ln_mask
    (time)
    bool
    True True True ... False False

    array([ True,  True,  True, ..., False, False, False])

  • warm_mask
    (time)
    bool
    True True True ... True True True

    array([ True,  True,  True, ...,  True,  True,  True])

  • cool_mask
    (time)
    bool
    False False False ... False False

    array([False, False, False, ..., False, False, False])

  • enso_transition
    (time)
    <U12
    'La-Nina warm' ... '____________'

    description :

    Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

    array(['La-Nina warm', 'La-Nina warm', 'La-Nina warm', ...,
           '____________', '____________', '____________'], dtype='<U12')

  • N2T_bins
    (N2T_bins)
    object
    [-5.0, -4.9) ... [-2.20000000000...

    long_name :

    log$_{10} 4N_T^2$

    array([Interval(-5.0, -4.9, closed='left'),
           Interval(-4.9, -4.800000000000001, closed='left'),
           Interval(-4.800000000000001, -4.700000000000001, closed='left'),
           Interval(-4.700000000000001, -4.600000000000001, closed='left'),
           Interval(-4.600000000000001, -4.500000000000002, closed='left'),
           Interval(-4.500000000000002, -4.400000000000002, closed='left'),
           Interval(-4.400000000000002, -4.3000000000000025, closed='left'),
           Interval(-4.3000000000000025, -4.200000000000003, closed='left'),
           Interval(-4.200000000000003, -4.100000000000003, closed='left'),
           Interval(-4.100000000000003, -4.0000000000000036, closed='left'),
           Interval(-4.0000000000000036, -3.900000000000004, closed='left'),
           Interval(-3.900000000000004, -3.8000000000000043, closed='left'),
           Interval(-3.8000000000000043, -3.7000000000000046, closed='left'),
           Interval(-3.7000000000000046, -3.600000000000005, closed='left'),
           Interval(-3.600000000000005, -3.5000000000000053, closed='left'),
           Interval(-3.5000000000000053, -3.4000000000000057, closed='left'),
           Interval(-3.4000000000000057, -3.300000000000006, closed='left'),
           Interval(-3.300000000000006, -3.2000000000000064, closed='left'),
           Interval(-3.2000000000000064, -3.1000000000000068, closed='left'),
           Interval(-3.1000000000000068, -3.000000000000007, closed='left'),
           Interval(-3.000000000000007, -2.9000000000000075, closed='left'),
           Interval(-2.9000000000000075, -2.800000000000008, closed='left'),
           Interval(-2.800000000000008, -2.700000000000008, closed='left'),
           Interval(-2.700000000000008, -2.6000000000000085, closed='left'),
           Interval(-2.6000000000000085, -2.500000000000009, closed='left'),
           Interval(-2.500000000000009, -2.4000000000000092, closed='left'),
           Interval(-2.4000000000000092, -2.3000000000000096, closed='left'),
           Interval(-2.3000000000000096, -2.20000000000001, closed='left'),
           Interval(-2.20000000000001, -2.1000000000000103, closed='left')],
          dtype=object)

  • S2_bins
    (S2_bins)
    object
    [-5.0, -4.9) ... [-2.20000000000...

    long_name :

    $S^2$

    array([Interval(-5.0, -4.9, closed='left'),
           Interval(-4.9, -4.800000000000001, closed='left'),
           Interval(-4.800000000000001, -4.700000000000001, closed='left'),
           Interval(-4.700000000000001, -4.600000000000001, closed='left'),
           Interval(-4.600000000000001, -4.500000000000002, closed='left'),
           Interval(-4.500000000000002, -4.400000000000002, closed='left'),
           Interval(-4.400000000000002, -4.3000000000000025, closed='left'),
           Interval(-4.3000000000000025, -4.200000000000003, closed='left'),
           Interval(-4.200000000000003, -4.100000000000003, closed='left'),
           Interval(-4.100000000000003, -4.0000000000000036, closed='left'),
           Interval(-4.0000000000000036, -3.900000000000004, closed='left'),
           Interval(-3.900000000000004, -3.8000000000000043, closed='left'),
           Interval(-3.8000000000000043, -3.7000000000000046, closed='left'),
           Interval(-3.7000000000000046, -3.600000000000005, closed='left'),
           Interval(-3.600000000000005, -3.5000000000000053, closed='left'),
           Interval(-3.5000000000000053, -3.4000000000000057, closed='left'),
           Interval(-3.4000000000000057, -3.300000000000006, closed='left'),
           Interval(-3.300000000000006, -3.2000000000000064, closed='left'),
           Interval(-3.2000000000000064, -3.1000000000000068, closed='left'),
           Interval(-3.1000000000000068, -3.000000000000007, closed='left'),
           Interval(-3.000000000000007, -2.9000000000000075, closed='left'),
           Interval(-2.9000000000000075, -2.800000000000008, closed='left'),
           Interval(-2.800000000000008, -2.700000000000008, closed='left'),
           Interval(-2.700000000000008, -2.6000000000000085, closed='left'),
           Interval(-2.6000000000000085, -2.500000000000009, closed='left'),
           Interval(-2.500000000000009, -2.4000000000000092, closed='left'),
           Interval(-2.4000000000000092, -2.3000000000000096, closed='left'),
           Interval(-2.3000000000000096, -2.20000000000001, closed='left'),
           Interval(-2.20000000000001, -2.1000000000000103, closed='left')],
          dtype=object)

  • enso_transition_phase
    (enso_transition_phase)
    object
    'none' 'El-Nino cool' ... 'all'

    description :

    Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

    array(['none', 'El-Nino cool', 'El-Nino warm', 'La-Nina cool', 'La-Nina warm',
           '____________', 'all'], dtype=object)

  • stat
    (stat)
    object
    'mean' 'count'

    array(['mean', 'count'], dtype=object)

  • N2_bins
    (N2_bins)
    object
    [-5.0, -4.9) ... [-2.20000000000...

    long_name :

    $N^2$

    array([Interval(-5.0, -4.9, closed='left'),
           Interval(-4.9, -4.800000000000001, closed='left'),
           Interval(-4.800000000000001, -4.700000000000001, closed='left'),
           Interval(-4.700000000000001, -4.600000000000001, closed='left'),
           Interval(-4.600000000000001, -4.500000000000002, closed='left'),
           Interval(-4.500000000000002, -4.400000000000002, closed='left'),
           Interval(-4.400000000000002, -4.3000000000000025, closed='left'),
           Interval(-4.3000000000000025, -4.200000000000003, closed='left'),
           Interval(-4.200000000000003, -4.100000000000003, closed='left'),
           Interval(-4.100000000000003, -4.0000000000000036, closed='left'),
           Interval(-4.0000000000000036, -3.900000000000004, closed='left'),
           Interval(-3.900000000000004, -3.8000000000000043, closed='left'),
           Interval(-3.8000000000000043, -3.7000000000000046, closed='left'),
           Interval(-3.7000000000000046, -3.600000000000005, closed='left'),
           Interval(-3.600000000000005, -3.5000000000000053, closed='left'),
           Interval(-3.5000000000000053, -3.4000000000000057, closed='left'),
           Interval(-3.4000000000000057, -3.300000000000006, closed='left'),
           Interval(-3.300000000000006, -3.2000000000000064, closed='left'),
           Interval(-3.2000000000000064, -3.1000000000000068, closed='left'),
           Interval(-3.1000000000000068, -3.000000000000007, closed='left'),
           Interval(-3.000000000000007, -2.9000000000000075, closed='left'),
           Interval(-2.9000000000000075, -2.800000000000008, closed='left'),
           Interval(-2.800000000000008, -2.700000000000008, closed='left'),
           Interval(-2.700000000000008, -2.6000000000000085, closed='left'),
           Interval(-2.6000000000000085, -2.500000000000009, closed='left'),
           Interval(-2.500000000000009, -2.4000000000000092, closed='left'),
           Interval(-2.4000000000000092, -2.3000000000000096, closed='left'),
           Interval(-2.3000000000000096, -2.20000000000001, closed='left'),
           Interval(-2.20000000000001, -2.1000000000000103, closed='left')],
          dtype=object)

  • bin_areas
    (N2T_bins, S2_bins)
    float64
    0.01 0.01 0.01 ... 0.01 0.01 0.01

    long_name :

    log$_{10} 4N_T^2$

    array([[0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01],
           [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01],
           [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01],
           [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01],
           [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01],
           [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01],
           [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
    ...
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01],
           [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01],
           [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01],
           [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01],
           [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01],
           [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01],
           [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
            0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]])

  • Rig_T_bins
    (Rig_T_bins)
    object
    (0.025118864315095794, 0.0398107...

    long_name :

    $Ri^g_T$

    array([Interval(0.025118864315095794, 0.03981071705534971, closed='right'),
           Interval(0.03981071705534971, 0.0630957344480193, closed='right'),
           Interval(0.0630957344480193, 0.09999999999999995, closed='right'),
           Interval(0.09999999999999995, 0.15848931924611126, closed='right'),
           Interval(0.15848931924611126, 0.25118864315095785, closed='right'),
           Interval(0.25118864315095785, 0.3981071705534969, closed='right'),
           Interval(0.3981071705534969, 0.6309573444801927, closed='right'),
           Interval(0.6309573444801927, 0.999999999999999, closed='right'),
           Interval(0.999999999999999, 1.5848931924611116, closed='right')],
          dtype=object)

• Data variables: (41)
  • N2
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    long_name :

    $N^2$

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

  • N2T
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    long_name :

    $N^2_T$

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

  • Ri
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    long_name :

    $Ri_g$

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

  • Rig_T
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    long_name :

    $Ri^g_T$

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 7 graph layers Data type float64 numpy.ndarray

  • S
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    41

    generic_name :

    sal

    long_name :

    SALINITY (PSU)

    name :

    S

    standard_name :

    sea_water_salinity

    units :

    PSU

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

  • S2
    (time, depth)
    float32
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    long_name :

    $S^2$

    Array Chunk Bytes 49.40 MiB 23.27 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float32 numpy.ndarray

  • T
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    FORTRAN_format :

    f10.2

    epic_code :

    20

    generic_name :

    temp

    long_name :

    TEMPERATURE (C)

    name :

    T

    standard_name :

    sea_water_temperature

    units :

    C

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

  • dens
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    long_name :

    $ρ$

    standard_name :

    sea_water_potential_density

    units :

    kg/m3

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

  • densT
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    description :

    density using T, S

    long_name :

    $ρ_T$

    standard_name :

    sea_water_potential_density

    units :

    kg/m3

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

  • lwnet
    (time)
    float32
    dask.array<chunksize=(212302,), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    1136

    generic_name :

    qln

    long_name :

    NET LONGWAVE RADIATION

    name :

    LWN

    units :

    W m-2

    Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

  • qlat
    (time)
    float32
    dask.array<chunksize=(212302,), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    137

    generic_name :

    qlat

    long_name :

    LATENT HEAT FLUX

    name :

    QL

    units :

    W m-2

    Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

  • qnet
    (time)
    float32
    dask.array<chunksize=(212302,), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    210

    generic_name :

    qtot

    long_name :

    TOTAL HEAT FLUX

    name :

    QT

    units :

    W/M**2

    standard_name :

    surface_downward_heat_flux_in_sea_water

    Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

  • qsen
    (time)
    float32
    dask.array<chunksize=(212302,), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    138

    generic_name :

    qsen

    long_name :

    SENSIBLE HEAT FLUX

    name :

    QS

    units :

    W m-2

    Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

  • swnet
    (time)
    float32
    dask.array<chunksize=(212302,), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    1495

    generic_name :

    sw

    long_name :

    NET SHORTWAVE RADIATION

    name :

    SWN

    units :

    W/M**2

    standard_name :

    net_downward_shortwave_flux_at_sea_water_surface

    Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

  • tau
    (time)
    float32
    dask.array<chunksize=(212302,), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    440

    generic_name :

    tau

    long_name :

    WIND STRESS

    name :

    TAU

    units :

    N/m2

    Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

  • taudir
    (time)
    float32
    dask.array<chunksize=(212302,), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    445

    generic_name :

    td

    long_name :

    WIND STRESS DIRECTION

    name :

    TD

    units :

    degrees

    Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

  • taux
    (time)
    float32
    dask.array<chunksize=(212302,), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    442

    generic_name :

    tx

    long_name :

    ZONAL WIND STRESS

    name :

    TX

    standard_name :

    surface_downward_eastward_stress

    units :

    N/m2

    Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

  • tauy
    (time)
    float32
    dask.array<chunksize=(212302,), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    443

    generic_name :

    ty

    long_name :

    MERID WIND STRESS

    name :

    TY

    standard_name :

    surface_downward_northward_stress

    units :

    N/m2

    Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

  • theta
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    description :

    potential temperature using T, S=35

    long_name :

    $θ$

    standard_name :

    sea_water_potential_temperature

    units :

    degC

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

  • u
    (time, depth)
    float32
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    1205

    generic_name :

    u

    long_name :

    u

    name :

    u

    standard_name :

    sea_water_x_velocity

    units :

    m/s

    Array Chunk Bytes 49.40 MiB 23.27 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float32 numpy.ndarray

  • v
    (time, depth)
    float32
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    1206

    generic_name :

    v

    long_name :

    v

    name :

    v

    standard_name :

    sea_water_y_velocity

    units :

    m/s

    Array Chunk Bytes 49.40 MiB 23.27 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float32 numpy.ndarray

  • pressure
    (depth)
    float64
    301.9 296.8 291.8 ... 5.028 -0.0

    standard_name :

    sea_water_pressure

    units :

    dbar

    array([301.87732362, 296.84242473, 291.80764803, 286.77299352,
           281.73846121, 276.70405112, 271.66976325, 266.63559761,
           261.60155422, 256.56763308, 251.5338342 , 246.5001576 ,
           241.46660329, 236.43317126, 231.39986155, 226.36667414,
           221.33360906, 216.30066632, 211.26784592, 206.23514788,
           201.2025722 , 196.17011889, 191.13778797, 186.10557945,
           181.07349333, 176.04152963, 171.00968835, 165.97796951,
           160.94637311, 155.91489917, 150.8835477 , 145.8523187 ,
           140.82121218, 135.79022817, 130.75936665, 125.72862766,
           120.69801119, 115.66751726, 110.63714587, 105.60689704,
           100.57677078,  95.54676709,  90.51688599,  85.48712749,
            80.4574916 ,  75.42797832,  70.39858766,  65.36931965,
            60.34017428,  55.31115157,  50.28225153,  45.25347416,
            40.22481948,  35.1962875 ,  30.16787822,  25.13959167,
            20.11142784,  15.08338675,  10.0554684 ,   5.02767282,
            -0.        ])

  • SA
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    standard_name :

    sea_water_absolute_salinity

    units :

    g/kg

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 5 graph layers Data type float64 numpy.ndarray

  • CT
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    standard_name :

    sea_water_conservative_temperature

    units :

    degC

    reference_scale :

    ITS-90

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 9 graph layers Data type float64 numpy.ndarray

  • α
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    units :

    1/K

    standard_name :

    sea_water_thermal_expansion_coefficient

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 10 graph layers Data type float64 numpy.ndarray

  • β
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    units :

    kg/g

    standard_name :

    sea_water_haline_contraction_coefficient

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 10 graph layers Data type float64 numpy.ndarray

  • Tz
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    long_name :

    $T_z$

    units :

    ℃/m

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 8 graph layers Data type float64 numpy.ndarray

  • Sz
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    long_name :

    $S_z$

    units :

    g/kg/m

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 8 graph layers Data type float64 numpy.ndarray

  • chi
    (time, depthchi)
    float64
    nan nan nan nan ... nan nan nan nan

    array([[nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           ...,
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan]])

  • KT
    (time, depthchi)
    float64
    nan nan nan nan ... nan nan nan nan

    standard_name :

    ocean_vertical_heat_diffusivity

    array([[nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           ...,
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan]])

  • eps
    (time, depthchi)
    float64
    nan nan nan nan ... nan nan nan nan

    array([[nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           ...,
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan]])

  • Jq
    (time, depthchi)
    float64
    nan nan nan nan ... nan nan nan nan

    array([[nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           ...,
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan]])

  • shred2
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    long_name :

    $Sh_{red}^2$

    units :

    $s^{-2}$

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 8 graph layers Data type float64 numpy.ndarray

  • Rig
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    long_name :

    $Ri^g$

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 7 graph layers Data type float64 numpy.ndarray

  • sst
    (time)
    float64
    dask.array<chunksize=(33130,), meta=np.ndarray>

    description :

    potential temperature using T, S=35

    long_name :

    $SST$

    standard_name :

    sea_surface_temperature

    units :

    degC

    Array Chunk Bytes 1.62 MiB 781.25 kiB Shape (212302,) (100000,) Dask graph 3 chunks in 4 graph layers Data type float64 numpy.ndarray

  • Tflx_dia_diff
    (time, depthchi)
    float64
    nan nan nan nan ... nan nan nan nan

    standard_name :

    ocean_vertical_diffusive_heat_flux

    array([[nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           ...,
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan]])

  • Jb
    (time, depthchi, depth)
    float64
    dask.array<chunksize=(33130, 7, 61), meta=np.ndarray>

    standard_name :

    ocean_vertical_diffusive_buoyancy_flux

    Array Chunk Bytes 691.63 MiB 325.78 MiB Shape (212302, 7, 61) (100000, 7, 61) Dask graph 3 chunks in 39 graph layers Data type float64 numpy.ndarray

  • Rif
    (time, depthchi, depth)
    float64
    dask.array<chunksize=(33130, 7, 61), meta=np.ndarray>

    standard_name :

    flux_richardson_number

    Array Chunk Bytes 691.63 MiB 325.78 MiB Shape (212302, 7, 61) (100000, 7, 61) Dask graph 3 chunks in 42 graph layers Data type float64 numpy.ndarray

  • n2s2pdf
    (N2T_bins, S2_bins, enso_transition_phase)
    float64
    dask.array<chunksize=(29, 29, 1), meta=np.ndarray>

    long_name :

    $P(S^2, 4N_T^2)$

    Array Chunk Bytes 45.99 kiB 32.85 kiB Shape (29, 29, 7) (29, 29, 5) Dask graph 3 chunks in 83 graph layers Data type float64 numpy.ndarray

  • eps_n2s2
    (stat, N2_bins, S2_bins, enso_transition_phase)
    float64
    dask.array<chunksize=(1, 29, 29, 7), meta=np.ndarray>

    Array Chunk Bytes 91.98 kiB 45.99 kiB Shape (2, 29, 29, 7) (1, 29, 29, 7) Dask graph 2 chunks in 94 graph layers Data type float64 numpy.ndarray

  • eps_ri
    (stat, Rig_T_bins, enso_transition_phase)
    float64
    dask.array<chunksize=(1, 9, 1), meta=np.ndarray>

    Array Chunk Bytes 0.98 kiB 432 B Shape (2, 9, 7) (1, 9, 6) Dask graph 4 chunks in 66 graph layers Data type float64 numpy.ndarray

• Indexes: (9)
  • depth
    PandasIndex

    PandasIndex(Float64Index([-300.0, -295.0, -290.0, -285.0, -280.0, -275.0, -270.0, -265.0,
                  -260.0, -255.0, -250.0, -245.0, -240.0, -235.0, -230.0, -225.0,
                  -220.0, -215.0, -210.0, -205.0, -200.0, -195.0, -190.0, -185.0,
                  -180.0, -175.0, -170.0, -165.0, -160.0, -155.0, -150.0, -145.0,
                  -140.0, -135.0, -130.0, -125.0, -120.0, -115.0, -110.0, -105.0,
                  -100.0,  -95.0,  -90.0,  -85.0,  -80.0,  -75.0,  -70.0,  -65.0,
                   -60.0,  -55.0,  -50.0,  -45.0,  -40.0,  -35.0,  -30.0,  -25.0,
                   -20.0,  -15.0,  -10.0,   -5.0,    0.0],
                 dtype='float64', name='depth'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1996-01-01 00:00:00', '1996-01-01 01:00:00',
                   '1996-01-01 02:00:00', '1996-01-01 03:00:00',
                   '1996-01-01 04:00:00', '1996-01-01 05:00:00',
                   '1996-01-01 06:00:00', '1996-01-01 07:00:00',
                   '1996-01-01 08:00:00', '1996-01-01 09:00:00',
                   ...
                   '2020-03-20 12:00:00', '2020-03-20 13:00:00',
                   '2020-03-20 14:00:00', '2020-03-20 15:00:00',
                   '2020-03-20 16:00:00', '2020-03-20 17:00:00',
                   '2020-03-20 18:00:00', '2020-03-20 19:00:00',
                   '2020-03-20 20:00:00', '2020-03-20 21:00:00'],
                  dtype='datetime64[ns]', name='time', length=212302, freq=None))

  • depthchi
    PandasIndex

    PandasIndex(Float64Index([-119.0, -89.0, -69.0, -59.0, -49.0, -39.0, -29.0], dtype='float64', name='depthchi'))

  • N2T_bins
    PandasIndex

    PandasIndex(Index([                              [-5.0, -4.9),
                           [-4.9, -4.800000000000001),
             [-4.800000000000001, -4.700000000000001),
             [-4.700000000000001, -4.600000000000001),
             [-4.600000000000001, -4.500000000000002),
             [-4.500000000000002, -4.400000000000002),
            [-4.400000000000002, -4.3000000000000025),
            [-4.3000000000000025, -4.200000000000003),
             [-4.200000000000003, -4.100000000000003),
            [-4.100000000000003, -4.0000000000000036),
            [-4.0000000000000036, -3.900000000000004),
            [-3.900000000000004, -3.8000000000000043),
           [-3.8000000000000043, -3.7000000000000046),
            [-3.7000000000000046, -3.600000000000005),
            [-3.600000000000005, -3.5000000000000053),
           [-3.5000000000000053, -3.4000000000000057),
            [-3.4000000000000057, -3.300000000000006),
            [-3.300000000000006, -3.2000000000000064),
           [-3.2000000000000064, -3.1000000000000068),
            [-3.1000000000000068, -3.000000000000007),
            [-3.000000000000007, -2.9000000000000075),
            [-2.9000000000000075, -2.800000000000008),
             [-2.800000000000008, -2.700000000000008),
            [-2.700000000000008, -2.6000000000000085),
            [-2.6000000000000085, -2.500000000000009),
            [-2.500000000000009, -2.4000000000000092),
           [-2.4000000000000092, -2.3000000000000096),
             [-2.3000000000000096, -2.20000000000001),
             [-2.20000000000001, -2.1000000000000103)],
          dtype='object', name='N2T_bins'))

  • S2_bins
    PandasIndex

    PandasIndex(Index([                              [-5.0, -4.9),
                           [-4.9, -4.800000000000001),
             [-4.800000000000001, -4.700000000000001),
             [-4.700000000000001, -4.600000000000001),
             [-4.600000000000001, -4.500000000000002),
             [-4.500000000000002, -4.400000000000002),
            [-4.400000000000002, -4.3000000000000025),
            [-4.3000000000000025, -4.200000000000003),
             [-4.200000000000003, -4.100000000000003),
            [-4.100000000000003, -4.0000000000000036),
            [-4.0000000000000036, -3.900000000000004),
            [-3.900000000000004, -3.8000000000000043),
           [-3.8000000000000043, -3.7000000000000046),
            [-3.7000000000000046, -3.600000000000005),
            [-3.600000000000005, -3.5000000000000053),
           [-3.5000000000000053, -3.4000000000000057),
            [-3.4000000000000057, -3.300000000000006),
            [-3.300000000000006, -3.2000000000000064),
           [-3.2000000000000064, -3.1000000000000068),
            [-3.1000000000000068, -3.000000000000007),
            [-3.000000000000007, -2.9000000000000075),
            [-2.9000000000000075, -2.800000000000008),
             [-2.800000000000008, -2.700000000000008),
            [-2.700000000000008, -2.6000000000000085),
            [-2.6000000000000085, -2.500000000000009),
            [-2.500000000000009, -2.4000000000000092),
           [-2.4000000000000092, -2.3000000000000096),
             [-2.3000000000000096, -2.20000000000001),
             [-2.20000000000001, -2.1000000000000103)],
          dtype='object', name='S2_bins'))

  • enso_transition_phase
    PandasIndex

    PandasIndex(Index(['none', 'El-Nino cool', 'El-Nino warm', 'La-Nina cool', 'La-Nina warm',
           '____________', 'all'],
          dtype='object', name='enso_transition_phase'))

  • stat
    PandasIndex

    PandasIndex(Index(['mean', 'count'], dtype='object', name='stat'))

  • N2_bins
    PandasIndex

    PandasIndex(Index([                              [-5.0, -4.9),
                           [-4.9, -4.800000000000001),
             [-4.800000000000001, -4.700000000000001),
             [-4.700000000000001, -4.600000000000001),
             [-4.600000000000001, -4.500000000000002),
             [-4.500000000000002, -4.400000000000002),
            [-4.400000000000002, -4.3000000000000025),
            [-4.3000000000000025, -4.200000000000003),
             [-4.200000000000003, -4.100000000000003),
            [-4.100000000000003, -4.0000000000000036),
            [-4.0000000000000036, -3.900000000000004),
            [-3.900000000000004, -3.8000000000000043),
           [-3.8000000000000043, -3.7000000000000046),
            [-3.7000000000000046, -3.600000000000005),
            [-3.600000000000005, -3.5000000000000053),
           [-3.5000000000000053, -3.4000000000000057),
            [-3.4000000000000057, -3.300000000000006),
            [-3.300000000000006, -3.2000000000000064),
           [-3.2000000000000064, -3.1000000000000068),
            [-3.1000000000000068, -3.000000000000007),
            [-3.000000000000007, -2.9000000000000075),
            [-2.9000000000000075, -2.800000000000008),
             [-2.800000000000008, -2.700000000000008),
            [-2.700000000000008, -2.6000000000000085),
            [-2.6000000000000085, -2.500000000000009),
            [-2.500000000000009, -2.4000000000000092),
           [-2.4000000000000092, -2.3000000000000096),
             [-2.3000000000000096, -2.20000000000001),
             [-2.20000000000001, -2.1000000000000103)],
          dtype='object', name='N2_bins'))

  • Rig_T_bins
    PandasIndex

    PandasIndex(Index([(0.025118864315095794, 0.03981071705534971],
             (0.03981071705534971, 0.0630957344480193],
             (0.0630957344480193, 0.09999999999999995],
            (0.09999999999999995, 0.15848931924611126],
            (0.15848931924611126, 0.25118864315095785],
             (0.25118864315095785, 0.3981071705534969],
              (0.3981071705534969, 0.6309573444801927],
               (0.6309573444801927, 0.999999999999999],
               (0.999999999999999, 1.5848931924611116]],
          dtype='object', name='Rig_T_bins'))

• Attributes: (10)

  CREATION_DATE :

  23:26 24-FEB-2021

  Data_Source :

  Global Tropical Moored Buoy Array Project Office/NOAA/PMEL

  File_info :

  Contact: Dai.C.McClurg@noaa.gov

  Request_for_acknowledgement :

  If you use these data in publications or presentations, please acknowledge the GTMBA Project Office of NOAA/PMEL. Also, we would appreciate receiving a preprint and/or reprint of publications utilizing the data for inclusion in our bibliography. Relevant publications should be sent to: GTMBA Project Office, NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115

  _FillValue :

  1.0000000409184788e+35

  array :

  TAO/TRITON

  missing_value :

  1.0000000409184788e+35

  platform_code :

  0n165e

  site_code :

  0n165e

  wmo_platform_code :

  52321

def plot_eps_ri_hist(eps_ri):
    import pandas as pd
    import hvplot.pandas

    intervals = pd.Index(pd.arrays.IntervalArray(eps_ri.Rig_T_bins.data)).mid

    eps_ri = eps_ri.copy()
    eps_ri["Rig_T_bins"] = intervals

    # df = eps_ri.load().sel(
    #    stat="mean", enso_transition_phase=["La-Nina cool", "El-Nino warm"]
    # ).to_dataframe()
    # new_midx = pd.MultiIndex.from_product(
    #    [intervals, ["La-Nina cool", "El-Nino warm"]],
    #    names=["Rig_T_bins", "enso_transition_phase"]),
    # df.index = new_midx

    step = (
        eps_ri.sel(stat="mean")
        .sel(
            enso_transition_phase=[
                "El-Nino warm",  # "El-Nino cool",
                "La-Nina cool",
                # "La-Nina warm"
            ]
        )
        .hvplot.step(
            frame_width=600,
            frame_height=300,
            col="enso_transition_phase",
            x="Rig_T_bins",
            logx=True,
            logy=True,
        )
    )

    return step.opts(
        hv.opts.Curve(xlim=(None, 1.2), xticks=[0.04, 0.1, 0.25, 0.5, 0.63, 1.6])
    )


plot_eps_ri_hist(tao_gridded.eps_ri)

%autoreload

newda = xr.DataArray(
    np.log10(
        tao_gridded.eps_ri.sel(
            enso_transition_phase=["La-Nina cool", "El-Nino warm"], stat="mean"
        )
    )
)

f, ax = plt.subplots(1, 1, constrained_layout=True)
newda.plot.step(hue="enso_transition_phase", xscale="log", ylim=(-7, -6))
ticks = [0.04, 0.1, 0.25, 0.63, 1.6]
ax.set_xticks(ticks)
ax.set_xticklabels([0.04, 0.1, 0.25, 0.63, 1.6])
dcpy.plots.linex(0.25)

Moum et al (2013) Climatologies

• The published .nc file seems “over cleaned” with mask ε < 3e-6 or so

tropflux = xr.open_mfdataset(
    sorted(glob.glob("/glade/work/dcherian/datasets/tropflux/**/*.nc")),
    engine="netcdf4",
    parallel=True,
)

tropflux_0140 = tropflux.interp(latitude=0, longitude=360 - 140).load()
tropflux_clim = tropflux_0140.groupby("time.month").mean()

Monthly climatology, Vertical mean

Didn’t work with published nc file

clim = xr.Dataset()
clim["netflux"] = (
    tropflux_0140.netflux.sel(time=slice("2005", "2011")).groupby("time.month").mean()
)
clim["Ih"] = tao_gridded.Ih.sel(time=slice("2005", "2011")).groupby("time.month").mean()
clim["Jq"] = (
    tao_gridded.Jq.sel(time=slice("2005", "2011"), depthchi=slice(-60, -20))
    .groupby("time.month")
    .mean(["depthchi", "time"])
)

(-1 * clim.Jq).plot(ylim=(0, 200))
(clim.netflux - clim.Ih).plot(ylim=(0, 200))

[<matplotlib.lines.Line2D>]

Try with .mat file

cchdo = dcpy.oceans.read_cchdo_chipod_file(
    "/glade/u/home/dcherian/datasets/microstructure/osu/chipod/tao/chipods_0_140W.nc"
)
cchdo

<xarray.Dataset>
Dimensions:     (depth: 7, time: 107588)
Coordinates:
    timeSeries  (depth) float64 ...
  * time        (time) datetime64[ns] 2005-09-23T04:30:00 ... 2017-12-31T23:2...
    lat         (depth) float64 dask.array<chunksize=(7,), meta=np.ndarray>
    lon         (depth) float64 dask.array<chunksize=(7,), meta=np.ndarray>
  * depth       (depth) float64 29.0 39.0 49.0 59.0 69.0 89.0 119.0
Data variables:
    T           (depth, time) float64 dask.array<chunksize=(7, 10000), meta=np.ndarray>
    dTdz        (depth, time) float64 dask.array<chunksize=(7, 10000), meta=np.ndarray>
    N2          (depth, time) float64 dask.array<chunksize=(7, 10000), meta=np.ndarray>
    KT          (depth, time) float64 dask.array<chunksize=(7, 10000), meta=np.ndarray>
    chi         (depth, time) float64 dask.array<chunksize=(7, 10000), meta=np.ndarray>
    eps         (depth, time) float64 dask.array<chunksize=(7, 10000), meta=np.ndarray>
    Jq          (depth, time) float64 dask.array<chunksize=(7, 10000), meta=np.ndarray>
Attributes: (12/68)
    ncei_template_version:           NCEI_NetCDF_TimeSeries_Orthogonal_Templa...
    featureType:                     timeSeries
    title:                           Turbulence quantities measured by chipod...
    summary:                         Turbulence data in upper w m / all good ...
    keywords:                        sea_water_temperature, square_of_brunt_v...
    Conventions:                     CF-1.6, ACDD-1.3
    ...                              ...
    reference8:                      Moum, J.N., Ocean speed and turbulence m...
    reference9:                      Warner, S.J., J. Becherer, K. Pujiana, E...
    reference10:                     Moum, J.N., K. Pujiana, R-C. Lien and W....
    reference11:                     Becherer, J. and J.N. Moum, An efficient...
    reference12:                     Moulin, A.J., J.N. Moum and E.L. Shroyer...
    reference13:                     Thakur, R., E.L. Shroyer, R. Govindaraja...

xarray.Dataset

• Dimensions:
  • depth: 7
  • time: 107588
• Coordinates: (5)
  • timeSeries
    (depth)
    float64
    ...

    long_name :

    depths of Time series of T, dTdz, Nsqr, chi, epsilon, and Jq along a mooring line at 0-140W

    cf_role :

    timeseries_id

    [7 values with dtype=float64]

  • time
    (time)
    datetime64[ns]
    2005-09-23T04:30:00 ... 2017-12-...

    long_name :

    Time, UTC

    standard_name :

    time

    axis :

    T

    array(['2005-09-23T04:30:00.000000000', '2005-09-23T05:29:59.999996672',
           '2005-09-23T06:30:00.000003328', ..., '2017-12-31T21:30:00.000003328',
           '2017-12-31T22:30:00.000000000', '2017-12-31T23:29:59.999996672'],
          dtype='datetime64[ns]')

  • lat
    (depth)
    float64
    dask.array<chunksize=(7,), meta=np.ndarray>

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    valid_min :

    0.0

    valid_max :

    0.0

    Array Chunk Bytes 56 B 56 B Shape (7,) (7,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lon
    (depth)
    float64
    dask.array<chunksize=(7,), meta=np.ndarray>

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    axis :

    X

    valid_min :

    -140.0

    valid_max :

    -140.0

    Array Chunk Bytes 56 B 56 B Shape (7,) (7,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • depth
    (depth)
    float64
    29.0 39.0 49.0 59.0 69.0 89.0 119.0

    long_name :

    depths of sensors along the mooring line

    standard_name :

    depth

    units :

    m

    axis :

    Z

    positive :

    down

    valid_min :

    29.0

    valid_max :

    119.0

    array([ 29.,  39.,  49.,  59.,  69.,  89., 119.])

• Data variables: (7)
  • T
    (depth, time)
    float64
    dask.array<chunksize=(7, 10000), meta=np.ndarray>

    long_name :

    sea water temperature

    standard_name :

    sea_water_temperature

    units :

    C

    FillValue :

    -9999

    valid_min :

    283.15

    valid_max :

    308.15

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    measured by fast thermistor FP07

    references :

    Moum, J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 5.75 MiB 546.88 kiB Shape (7, 107588) (7, 10000) Dask graph 11 chunks in 4 graph layers Data type float64 numpy.ndarray

  • dTdz
    (depth, time)
    float64
    dask.array<chunksize=(7, 10000), meta=np.ndarray>

    long_name :

    vertical temperature gradient

    units :

    K m-1

    FillValue :

    -9999

    valid_min :

    4.9999999999999996e-06

    valid_max :

    0.001

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    references :

    Moum, J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 5.75 MiB 546.88 kiB Shape (7, 107588) (7, 10000) Dask graph 11 chunks in 3 graph layers Data type float64 numpy.ndarray

  • N2
    (depth, time)
    float64
    dask.array<chunksize=(7, 10000), meta=np.ndarray>

    long_name :

    squared buoyancy frequency

    standard_name :

    square_of_brunt_vaisala_frequency_in_sea_water

    units :

    s-2

    FillValue :

    -9999

    valid_min :

    1e-10

    valid_max :

    0.001

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    references :

    Moum, J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 5.75 MiB 546.88 kiB Shape (7, 107588) (7, 10000) Dask graph 11 chunks in 3 graph layers Data type float64 numpy.ndarray

  • KT
    (depth, time)
    float64
    dask.array<chunksize=(7, 10000), meta=np.ndarray>

    long_name :

    turbulent diffusivity of temperature

    standard_name :

    ocean_vertical_heat_diffusivity

    units :

    m2 s-1

    FillValue :

    -9999

    valid_min :

    1e-10

    valid_max :

    0.1

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    inferred from fast thermistor spectral scaling

    references :

    Moum, J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 5.75 MiB 546.88 kiB Shape (7, 107588) (7, 10000) Dask graph 11 chunks in 3 graph layers Data type float64 numpy.ndarray

  • chi
    (depth, time)
    float64
    dask.array<chunksize=(7, 10000), meta=np.ndarray>

    long_name :

    dissipation of temperature variance

    units :

    K2 s-1

    FillValue :

    -9999

    valid_min :

    1.0000000000000001e-11

    valid_max :

    0.0001

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    inferred from fast thermistor spectral scaling

    references :

    Moum J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 5.75 MiB 546.88 kiB Shape (7, 107588) (7, 10000) Dask graph 11 chunks in 3 graph layers Data type float64 numpy.ndarray

  • eps
    (depth, time)
    float64
    dask.array<chunksize=(7, 10000), meta=np.ndarray>

    long_name :

    turbulence dissipation rate

    standard_name :

    specific_turbulent_kinetic_energy_dissipation_in_sea_water

    ncei_name :

    turbulent kinetic energy (TKE) dissipation rate

    units :

    W kg-1

    FillValue :

    -9999

    valid_min :

    1e-12

    valid_max :

    9.999999999999999e-06

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    inferred from fast thermistor spectral scaling

    references :

    Moum J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 5.75 MiB 546.88 kiB Shape (7, 107588) (7, 10000) Dask graph 11 chunks in 3 graph layers Data type float64 numpy.ndarray

  • Jq
    (depth, time)
    float64
    dask.array<chunksize=(7, 10000), meta=np.ndarray>

    long_name :

    turbulent heat flux

    units :

    W m-2

    FillValue :

    -9999

    valid_min :

    -800.0

    valid_max :

    0.0

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    inferred from fast thermistor spectral scaling

    references :

    Moum J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 5.75 MiB 546.88 kiB Shape (7, 107588) (7, 10000) Dask graph 11 chunks in 3 graph layers Data type float64 numpy.ndarray

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex([          '2005-09-23 04:30:00',
                   '2005-09-23 05:29:59.999996672',
                   '2005-09-23 06:30:00.000003328',
                             '2005-09-23 07:30:00',
                   '2005-09-23 08:29:59.999996672',
                   '2005-09-23 09:30:00.000003328',
                             '2005-09-23 10:30:00',
                   '2005-09-23 11:29:59.999996672',
                   '2005-09-23 12:30:00.000003328',
                             '2005-09-23 13:30:00',
                   ...
                   '2017-12-31 14:29:59.999996672',
                   '2017-12-31 15:30:00.000003328',
                             '2017-12-31 16:30:00',
                   '2017-12-31 17:29:59.999996672',
                   '2017-12-31 18:30:00.000003328',
                             '2017-12-31 19:30:00',
                   '2017-12-31 20:29:59.999996672',
                   '2017-12-31 21:30:00.000003328',
                             '2017-12-31 22:30:00',
                   '2017-12-31 23:29:59.999996672'],
                  dtype='datetime64[ns]', name='time', length=107588, freq=None))

  • depth
    PandasIndex

    PandasIndex(Float64Index([29.0, 39.0, 49.0, 59.0, 69.0, 89.0, 119.0], dtype='float64', name='depth'))

• Attributes: (68)

  ncei_template_version :

  NCEI_NetCDF_TimeSeries_Orthogonal_Template_v2.0

  featureType :

  timeSeries

  title :

  Turbulence quantities measured by chipods on a TAO mooring line at 0-140W from 23-Sep-2005 to 31-Dec-2017

  summary :

  Turbulence data in upper w m / all good data between 2005 and 2016 - to be updated as analysis progresses. Data gaps are due to many reasons including lost moorings.

  keywords :

  sea_water_temperature, square_of_brunt_vaisala_frequency_in_sea_water, ocean_vertical_heat_diffusivity, specific_turbulent_kinetic_energy_dissipation_in_sea_water

  Conventions :

  CF-1.6, ACDD-1.3

  naming_authority :

  edu.oregonstate.coas.mixing

  history :

  2019-07-08 10:19:13 -ncatted

  source :

  fast thermistors FP07

  processing_level :

  3: Variables mapped on uniform space-time grid from higher resolution

  comment :

  See reference1 through reference13

  acknowledgment :

  Funding from the National Science Foundation ... with in-kind support from NOAA in the form of shipping, handling, deployment, recovery

  license :

  no restrictions

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table v49

  date_created :

  2019-07-08T10:19:15415Z

  creator_name :

  Jim Moum / Sally Warner / Aurelie Moulin / Oregon State University Ocean Mixing Group

  creator_url :

  http://mixing.coas.oregonstate.edu

  institution :

  Ocean Mixing Group, College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, USA

  project :

  TAO, PIRATA

  publisher_name :

  Jim Moum / Sally Warner / Aurelie Moulin / Oregon State University Ocean Mixing Group

  publisher_url :

  http://mixing.coas.oregonstate.edu

  geospatial_bounds :

  POINT (0 -140)

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_bounds_vertical_crs :

  EPSG:5831

  geospatial_lat_min :

  0.0

  geospatial_lat_max :

  0.0

  geospatial_lon_min :

  -140.0

  geospatial_lon_max :

  -140.0

  geospatial_vertical_min :

  29.0

  geospatial_vertical_max :

  119.0

  geospatial_vertical_positive :

  down

  time_coverage_start :

  2005-09-23T04:30:00000Z

  time_coverage_end :

  2017-12-31T23:30:00000Z

  time_coverage_duration :

  P12Y3M7DT19H0M00S

  time_coverage_resolution :

  PT1H

  sea_name :

  Equatorial Pacific Ocean

  creator_type :

  group

  creator_institution :

  College of Earth, Ocean, and Atmospheric Science, Oregon State University

  publisher_type :

  group

  publisher_institution :

  College of Earth, Ocean, and Atmospheric Science, Oregon State University

  contributor_name :

  Jim Moum, Sally Warner, Sasha Perlin, Aurelie Moulin

  contributor_role :

  Processed and/or archived data

  geospatial_lat_units :

  degrees_north

  geospatial_lon_units :

  degrees_west

  geospatial_vertical_units :

  m

  date_modified :

  2019-07-08T10:19:27096Z

  date_issued :

  2019-07-08T10:19:27416Z

  date_metadata_modified :

  2019-07-08T10:19:27663Z

  product_version :

  v2.0

  keywords_vocabulary :

  CF Standard Names

  platform :

  TAO mooring

  platform_vocabulary :

  NCEI Ocean Archive System Database

  instrument :

  chipod

  instrument_vocabulary :

  instrument designed by the Ocean Mixing Group at Oregon State University to measure turbulence quantities (http://mixing.coas.oregonstate.edu)

  cdm_data_type :

  Station

  reference1 :

  Moum, J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

  reference2 :

  Zhang, Y. and J.N. Moum, Inertial-convective subrange estimates of thermal variance dissipation rate from moored temperature measurements, J. Atmos. and Oceanic Tech., 27, 1950-1959, 2010. pdf: http://mixing.coas.oregonstate.edu/papers/zhang&moum2010.pdf

  reference3 :

  Moum, J.N., J.D. Nash and W.D. Smyth, Narrowband high-frequency oscillations at the equator. Part I: Interpretation as shear instabilities, J. Phys. Oceanogr, 41, 397-411, 2011. pdf: http://mixing.coas.oregonstate.edu/papers/2010JPO4450.pdf

  reference4 :

  Smyth, W.D., J.N. Moum and J.D. Nash, Narrowband high-frequency oscillations at the equator. Part II: Properties of shear instabilities, J. Phys. Oceanogr, 41, 412-428, 2011. pdf: http://mixing.coas.oregonstate.edu/papers/2010JPO4451.pdf

  reference5 :

  Perlin, A. and J.N. Moum, Comparison of Thermal Variance Dissipation Rates from Moored and Profiling Instruments at the Equator, J. Atmos. Oceanic Tech., 29, 1347-1362, 2012. pdf: http://mixing.coas.oregonstate.edu/papers/chipod_cham_chi_2012.pdf

  reference6 :

  Moum, J.N., A. Perlin, J.D. Nash and M.J. McPhaden, Seasonal sea surface cooling in the equatorial Pacific cold tongue controlled by ocean mixing. Nature, 500, 64-67. doi:10.1038/nature12363, 2013. pdf: https://www.nature.com/articles/nature12363

  reference7 :

  Smyth, W.D. and J.N. Moum, Marginal instability and deep cycle mixing in the eastern equatorial Pacific Ocean. Geophys Res. Lett., 40, 6181-6185, DOI: 10.1002/2013GL058403, 2013. pdf: http://mixing.coas.oregonstate.edu/papers/SmythMoum13_GRL_MI.pdf

  reference8 :

  Moum, J.N., Ocean speed and turbulence measurements using pitot-static tubes on moorings, J. Atmos. Oceanic Technol., 32, 1400-1413, doi:10.1175/JTECH-D-14-00158.1, 2015. pdf: https://journals.ametsoc.org/doi/full/10.1175/JTECH-D-14-00158.1

  reference9 :

  Warner, S.J., J. Becherer, K. Pujiana, E.L. Shroyer, M. Ravichandran, V.P. Thangaprakash and J.N. Moum, Monsoon mixing cycles in the Bay of Bengal: A year-long subsurface mixing record. Oceanography, 29(2):158?169, http://dx.doi.org/10.5670/oceanog.2016.4, 2016. pdf: http://www.tos.org/oceanography/article/monsoon-mixing-cycles-in-the-bay-of-bengal-a-year-long-subsurface-mixing-re

  reference10 :

  Moum, J.N., K. Pujiana, R-C. Lien and W.D. Smyth, Ocean feedback to pulses of the Madden-Julian Oscillation in the equatorial Indian Ocean. Nature Commun. 7, 13203 doi: 10.1038/ncomms13203, 2016. pdf: https://www.nature.com/articles/ncomms13203

  reference11 :

  Becherer, J. and J.N. Moum, An efficient scheme for onboard reduction of moored chipod data. J. Atmos. Oceanic Technol., 34, 2533-2546, doi:10.1175/JTECH-D-17-0118.1, 2017. pdf: https://journals.ametsoc.org/doi/full/10.1175/JTECH-D-17-0118.1

  reference12 :

  Moulin, A.J., J.N. Moum and E.L. Shroyer, Evolution of turbulence in the diurnal warm layer. J. Phys. Oceanogr., 48, 383-396, 2018. pdf: https://journals.ametsoc.org/doi/full/10.1175/JPO-D-17-0170.1

  reference13 :

  Thakur, R., E.L. Shroyer, R. Govindarajan, J.T. Farrar, R.A. Weller and J.N. Moum, Seasonality and buoyancy suppression of turbulence in the Bay of Bengal, Geophys. Res. Lett., https://doi.org/10.1029/2018GL081577, 2019. pdf: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018GL081577

mat = mixpods.read_chipod_mat_file(
    os.path.expanduser("~/work/pump/datasets/microstructure/chipods_0_140W_hourly.mat")
)

mat.Jq.where(np.abs(mat.Jq) < 10000).groupby("time.year").mean().sel(
    year=slice(2005, 2011)
).hvplot.line(
    by="year", y="depth", flip_yaxis=True
)  # .opts(legend_cols=1)

Compare

h1 = (
    cchdo.Jq.reset_coords(drop=True)
    .resample(time="M")
    .mean()
    .hvplot.quadmesh(
        cmap="greys",
        flip_yaxis=True,
        frame_width=1200,
        frame_height=200,
        clim=(40, -100),
    )
)
h2 = (
    mat.Jq.resample(time="M")
    .mean()
    .hvplot.quadmesh(
        cmap="greys",
        flip_yaxis=True,
        frame_width=1200,
        frame_height=200,
        clim=(40, -100),
    )
)

(h2.opts(title=".mat") + h1.opts(title="CCHDO")).cols(1)

h = (
    (
        np.abs(cchdo.Jq.resample(time="D").mean())
        .reset_coords(drop=True)
        .hvplot.line(by="depth", x="time")
        .opts(frame_width=1200)
        + np.abs(mat.Jq.resample(time="D").mean())
        .reset_coords(drop=True)
        .hvplot.line(by="depth", x="time")
        .opts(frame_width=1200)
    )
    .opts(shared_axes=True)
    .cols(1)
)
h

def plot_monthly_clim(ds, **kwargs):
    return np.abs(
        ds.Jq.sel(time=slice("2005", "2011-02"))
        .sel(depth=slice(20, 60))
        .mean("depth")
        .groupby("time.month")
        .mean()
    ).hvplot.line(**kwargs)


(
    plot_monthly_clim(mat, label=".mat")
    * plot_monthly_clim(mat.where(np.abs(mat.Jq) < 20000), label=".mat, Jq < 20000")
    * plot_monthly_clim(mat.where(np.abs(mat.Jq) < 10000), label=".mat, Jq < 10000")
    * plot_monthly_clim(mat.where(np.abs(mat.Jq) < 3000), label=".mat, Jq < 3000")
    * plot_monthly_clim(mat.where(mat.eps < 5e-4), label=".mat, ε < 5e-4")
    * plot_monthly_clim(mat.where(mat.eps < 1e-5), label=".mat, ε < 1e-5")
    * plot_monthly_clim(mat.where(mat.eps < 3e-6), label=".mat, ε < 3e-6")
    * plot_monthly_clim(cchdo, label="CCHDO", color="k")
    * (clim.netflux - clim.Ih)
    .rename("a")
    .hvplot.line(label="Jq0 - Ih", color="k", line_width=4)
).opts(show_grid=True, legend_position="right", frame_width=600, ylim=(0, 160))

Seasonal climatology, Vertical profile

def plot_seasonal_clim(ds, **kwargs):
    return (
        np.abs(
            ds.Jq.sel(time=slice("2005", "2011-02"))
            .sel(depth=slice(20, 70))
            # .mean("depth")
            .groupby("time.season")
            .mean()
            .sel(season=["DJF", "MAM", "JJA", "SON"])
        ).hvplot.line(flip_yaxis=True, y="depth", col="season", **kwargs)
        # .opts()
    )


h = (
    plot_seasonal_clim(mat, label=".mat")
    * plot_seasonal_clim(mat.where(mat.eps < 1e-4), label=".mat, ε < 1e-4")
    * plot_seasonal_clim(mat.where(mat.eps < 1e-5), label=".mat, ε < 1e-5")
    * plot_seasonal_clim(mat.where(mat.eps < 3e-6), label=".mat, ε < 3e-6")
    * plot_seasonal_clim(cchdo, label="CCHDO", color="k")
)
h.opts(plot_size=(270, 350), shared_xaxis=True, shared_yaxis=True, show_legend=True)

from cycler import cycler

with plt.rc_context({"axes.prop_cycle": cycler(color=["k", "r", "b", "g"])}):
    (
        (-1 * cchdo.Jq.where(cchdo.Jq > -1e3).squeeze())
        .sel(time=slice("2005", "2012-02-01"))
        .groupby("time.season")
        .mean()
        .sel(season=["DJF", "MAM", "JJA", "SON"])
        .cf.plot(y="depth", hue="season")
    )

Labeling ENSO phase transitions

pump.obs.process_oni().sel(time=slice("2005-Oct", "2016-Feb")).reset_coords(
    drop=True
).plot.line(aspect=3, size=5, color="k")
pump.obs.make_enso_transition_mask().sel(
    time=slice("2005-Oct", "2016-Feb")
).reset_coords(drop=True).plot.line(ax=plt.gca().twinx(), marker="x")
plt.grid(True, which="both", axis="both")

N2 vs N2T

Lot more data with N2T

tao_gridded.N2.cf.plot()

<matplotlib.collections.QuadMesh>

tao_gridded.N2T.cf.plot()

<matplotlib.collections.QuadMesh>

PDFs change

fg = tao_gridded.n2s2pdf.sel(
    enso_transition_phase=[
        "La-Nina cool",
        "La-Nina warm",
        "El-Nino warm",
        "El-Nino cool",
    ]
).plot(vmax=1, col="enso_transition_phase")
fg.set_titles("{value}")
fg.map(dcpy.plots.line45)
fg.axes[0, 0].set_xlim((-4.5, -2.5))
fg.axes[0, 0].set_ylim((-4.5, -2.5))

(-4.5, -2.5)

(
    tao_gridded.n2s2pdf.sel(
        enso_transition_phase=[
            "La-Nina cool",
            "La-Nina warm",
            "El-Nino warm",
            "El-Nino cool",
        ]
    )
    .sum("N2_bins")
    .plot(hue="enso_transition_phase")
)
plt.figure()
(
    tao_gridded.n2s2pdf.sel(
        enso_transition_phase=[
            "La-Nina cool",
            "La-Nina warm",
            "El-Nino warm",
            "El-Nino cool",
        ]
    )
    .sum("S2_bins")
    .plot(hue="enso_transition_phase")
)

[<matplotlib.lines.Line2D>,
 <matplotlib.lines.Line2D>,
 <matplotlib.lines.Line2D>,
 <matplotlib.lines.Line2D>]

fg = tao_gridded.n2s2Tpdf.sel(
    enso_transition_phase=[
        "La-Nina cool",
        "La-Nina warm",
        "El-Nino warm",
        "El-Nino cool",
    ]
).plot(vmax=1, col="enso_transition_phase")
fg.set_titles("{value}")
fg.map(dcpy.plots.line45)
fg.axes[0, 0].set_xlim((-4.5, -2.5))
fg.axes[0, 0].set_ylim((-4.5, -2.5))

(-4.5, -2.5)

(
    tao_gridded.n2s2Tpdf.sel(
        enso_transition_phase=[
            "La-Nina cool",
            "La-Nina warm",
            "El-Nino warm",
            "El-Nino cool",
        ]
    )
    .sum("N2_bins")
    .plot(hue="enso_transition_phase")
)
plt.figure()
(
    tao_gridded.n2s2Tpdf.sel(
        enso_transition_phase=[
            "La-Nina cool",
            "La-Nina warm",
            "El-Nino warm",
            "El-Nino cool",
        ]
    )
    .sum("S2_bins")
    .plot(hue="enso_transition_phase")
)

[<matplotlib.lines.Line2D>,
 <matplotlib.lines.Line2D>,
 <matplotlib.lines.Line2D>,
 <matplotlib.lines.Line2D>]